Dataset Information

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

ABSTRACT: Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

SUBMITTER: Jeong HH

PROVIDER: S-EPMC4491426 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Jeong Hyun-Hwan HH Leem Sangseob S Wee Kyubum K Sohn Kyung-Ah KA

Journal of ovarian research 20150703

<h4>Background</h4>Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, t ...[more]

PMID: 26138921

Similar Datasets

Project description:Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ~100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.

Project description:BackgroundThe increasing availability of multiple types of genomic profiles measured from the same cancer patients has provided numerous opportunities for investigating genomic mechanisms underlying cancer. In particular, association studies of gene expression traits with respect to multi-layered genomic features are highly useful for uncovering the underlying mechanism. Conventional correlation-based association tests are limited because they are prone to revealing indirect associations. Moreover, integration of multiple types of genomic features raises another challenge.MethodsIn this study, we propose a new framework for association studies called integrative regression network that identifies genomic associations on multiple high-dimensional genomic profiles by taking into account the associations between as well as within profiles. We employed high-dimensional regression techniques to first identify the associations between different genomic profiles. Based on the resulting regression coefficients, a regression network was constructed within each profile. For example, two methylation features having similar regression coefficients with respect to a number of gene expression traits are likely to be involved in the same biological process and therefore we define an edge between two methylation features in the regression network. To extract more reliable associations, multiple sparse structured regression techniques were applied and the resulting multiple networks were merged as the integrative regression network using a similarity network fusion technique.ResultsExperiments were carried out using four different sparse structured regression methods on five cancer types from TCGA. The advantages and disadvantages of each regression method were also explored. We find there was large inconsistency in the results from different regression methods, which supports the need to extract the proposed integrative regression network from multiple complimentary regression techniques. Fusing multiple regression networks by using similarity measurements led to the identification of significant gene pairs and a resulting network with better topological properties.ConclusionsWe developed and validated the integrative regression network scheme on multi-layered genomic profiles from TCGA. Our method facilitates identification of the strong signals as well as weaker signals by fusing information from different regression techniques. It could be extended to integrate results obtained from different cancer types as well.

Project description:Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFK?, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Project description:BackgroundOvarian (OV) cancer is considered as one of the most deadly malignancies in women, since it is unfortunately diagnosed in advanced stages. Nowadays, the importance of bioinformatics tools and their frequent usage in tracking dysregulated cancer-related genes and pathways have been highlighted in researches.AimThe aim of this study is to investigate dysregulated miRNAs-genes network and its function in OV tumors based on the integration of microarray data through a system biology approach.MethodsTwo microarray data (GSE119056 and GSE4122) were analyzed to explore the differentially expressed miRNAs (DEmiRs) and genes among OV tumors and normal tissues. Then, through the help of TargetScan, miRmap, and miRTarBase databases, the dysregulated miRNA-gene network in OV tumors was constructed by Cytoscape. In the next step, co-expression and protein-protein interaction networks were made using GEPIA and STRING databases. Moreover, the functional analysis of the hub genes was done by DAVID, KEGG, and Enrichr databases. Eventually, the regulatory network of TF-miRNA-gene was constructed.ResultsThe potential dysregulated miRNAs-genes network in OV tumors has been constructed, including 109 differentially expressed genes (DEGs), 25 DEmiRs, and 213 interactions. Two down-regulated microRNAs, miR-660-3p and hsa-miR-4510, have the most interactions with up-expressed oncogenic DEGs. CDK1, PLK1, CCNB1, CCNA2, and EZH2 are involved in protein module, which show significant overexpression in OV tumors according to The Cancer Genome Atlas (TCGA) data. EZH2 shows amplification in OV tumors with remarkable percentage. The transcription factors TFAP2C and GATA4 have the pivotal regulatory functions in oncotranscriptomic profile of OV tumors.ConclusionIn current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs-DEGs and TF-miRNA-gene networks reveal the potential interactions that could be a significant piece of the OV onco-puzzle.

Dataset Information

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Publications

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure